|Table of Contents|

Construction of a comprehensive prediction model for post-hepatectomy liver failure based on Gd-EOB-DTPA enhanced MRI

Journal Of Modern Oncology[ISSN:1672-4992/CN:61-1415/R]

Issue:
2023 16
Page:
3046-3050
Research Field:
Publishing date:

Info

Title:
Construction of a comprehensive prediction model for post-hepatectomy liver failure based on Gd-EOB-DTPA enhanced MRI
Author(s):
YIN Pengfei1; HUANG Guizhong2; LIN Qiaohong3; ZHANG Jianlong1
1.Department of Hepatobiliary Surgery,Sun Yat-sen Memorial Hospital,Sun Yat-sen University,Guangdong Guangzhou,510000,China;2.Department of Pancreaticobiliary Surgery;3.Department of Head and Neck Surgery,Sun Yat-sen University Cancer Center,Guangdong Guangzhou 510000,China.
Keywords:
Gd-EOB-DTPA enhanced MRI; signal intensity; post-hepatectomy liver failure; predictive model
PACS:
R735.7
DOI:
10.3969/j.issn.1672-4992.2023.16.019
Abstract:
Objective:To construct a predictive model for post-hepatectomy liver failure (PHLF) based on the signal intensity of Gd-EOB-DTPA enhanced MRI and clinical variables.Methods:The clinical data of 196 patients who underwent hepatectomy from January 2016 to December 2020 in our hospital were retrospectively analyzed,and the signal intensity of Gd-EOB-DTPA enhanced MRI images was measured.Independent high-risk factors for PHLF were identified by Logistic regression.A nomogram prediction model was constructed with the results of multivariable Logistic regression analysis,and the predictive ability of the model was validated using the concordance index,calibration curve,and decision curve analysis.Finally,the model was compared with other commonly used liver function assessment methods.Results:PHLF occurred in 62 patients (31.6%).Multivariable Logistic regression analysis identified prothrombin activity (PTA),operative time,and signal intensity of hepatobiliary phase (SIHBP) were independent risk factors for PHLF.The area under the curve (AUC) of the nomogram model was 0.810.The calibration curve and decision curve showed that the model had high predictive ability and clinical utility.The AUC showed that the model was significantly better than the ICGR-15 and other liver function assessment methods.Conclusion:The PHLF prediction model based on signal intensity in the hepatobiliary phase of Gd-EOB-DTPA enhanced MRI and clinical variables has better predictive performance.

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Memo

Memo:
National Natural Science Foundation of China(No.81672403）；国家自然科学基金资助项目(编号：81672403)
Last Update: 1900-01-01